code: def forward(
      self,
      query,
      key,
      value,
      key_padding_mask=None,
      need_weights=True,
      attn_mask=None,
      params=None,
  ):
      if params is None:
          params = OrderedDict(self.named_parameters())
  
      in_proj_weight = params.get("in_proj_weight", None)
      in_proj_bias = params.get("in_proj_bias", None)
      out_proj_bias = params.get("out_proj.bias", None)
      bias_k = params.get("bias_k", None)
      bias_v = params.get("bias_v", None)
  
      if not self._qkv_same_embed_dim:
          (
              attn_output,
              attn_output_weights,
          ) = F.multi_head_attention_forward(
              query,
              key,
              value,
              self.embed_dim,
              self.num_heads,
              in_proj_weight,
              in_proj_bias,
              bias_k,
              bias_v,
              self.add_zero_attn,
              self.dropout,
              params["out_proj.weight"],
              out_proj_bias,
              training=self.training,
              key_padding_mask=key_padding_mask,
              need_weights=need_weights,
              attn_mask=attn_mask,
              use_separate_proj_weight=True,
              q_proj_weight=params["q_proj_weight"],
              k_proj_weight=params["k_proj_weight"],
              v_proj_weight=params["v_proj_weight"],
          )
      else:
          (
              attn_output,
              attn_output_weights,
          ) = F.multi_head_attention_forward(
              query,
              key,
              value,
              self.embed_dim,
              self.num_heads,
              in_proj_weight,
              in_proj_bias,
              bias_k,
              bias_v,
              self.add_zero_attn,
              self.dropout,
              params["out_proj.weight"],
              out_proj_bias,
              training=self.training,
              key_padding_mask=key_padding_mask,
              need_weights=need_weights,
              attn_mask=attn_mask,
          )
      if self.batch_first:
          return attn_output.transpose(1, 0), attn_output_weights
      else:
          return attn_output, attn_output_weights
  
file_path: pytorch-meta/torchmeta/modules/activation.py
imports: import torch.nn
  import torch.nn.functional
  from collections import OrderedDict
  from torchmeta.modules.module import MetaModule
  from torchmeta.modules.linear import MetaLinear
label_desc: Use image node_id 2 for calling the MetaMultiheadAttention obj's underlying member method code with example usage: obj.forward(query, key, value, key_padding_mask, need_weights, attn_mask, params) and returns: attn_output,  attn_output_weights, attn_output,  attn_output_weights